This paper adapts a graph-based analysis and visualisation tool, search trajectory networks (STNs) to multi-objective combinatorial optimisation. We formally define multi-objective STNs and apply them to study the dynamics of two state-of-the-art multi-objective evolutionary algorithms: MOEA/D and NSGA2. In terms of benchmark, we consider two- and three-objective ρ mnk-landscapes for constructing multi-objective multi-modal landscapes with objective correlation. We find that STN metrics and visualisation offer valuable insights into both problem structure and algorithm performance. Most previous visual tools in multi-objective optimisation consider the objective space only. Instead, our newly proposed tool asses algorithm behaviour in the decision and objective spaces simultaneously.
CITATION STYLE
Ochoa, G., Liefooghe, A., Lavinas, Y., & Aranha, C. (2023). Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13987 LNCS, pp. 211–226). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-30035-6_14
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